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3/20/2004 7:48 PM
Note from a leading researcher in understanding knowledge and perception from an inferential and computational perspective, and BCNGroup response communicated to the 56 participates of a three-day conference at NIST organized by funding sources.
Dr. J.
We thank you for the kind note.
The thoughtfulness and themes in your note help us understand the situation that exists, with respect to future processes that may involve you or your students. It is understandable that you would have increasing funding given the quality of your research and your dedication. A great deal of value is obtained by finding functional decompositions of inferential actions by humans, what is called cognitive engineering at DARPA.
Our interest is not really to reform the system.
There are a lot of things in the world that are imperfect and the current R&D funding system is not as bad as many other things and has a lot of positive qualities.
Our problem is that the Nation needs a breakthrough that cannot occur as long as the AI entrenchment remains. As you so properly put it, “block heads in high places” reinforces the entrenchment. Our question as to why our society, and science, should have to put up with any poor behavior is informed by general systems theory.
Our position is that inference is an entailment, like physical cause. Human inference is only poorly reflected in classical logic, and the full nature of induction is barely touched by formal thought – in spite of its key role in axiomatics and theory proving. This position is inconsistent with 95% of the Artificial Intelligence curriculum taught in America’s colleges. The issue is simply ignored in the AI textbooks.
The work in AI is made with great dedication and is elegant. But AI is taught in computer science departments as a professional discipline to be learned as opposed to a natural science where principled investigation of natural reality grounds the future work of the student. As a consequence, the discipline of AI has been populated with tens of thousands of individuals who will not look at the concern from the natural sciences, and who are rewarded by this blindness by the persistent behavior of the federal funding sources and from the tenure system. This is unfortunate, but not necessary. The Nation can do better than this.
The fundamental blindness of AI researchers to the expressed concerns of the natural sciences has become a greater and greater problem over time, while federal funding continue to cause a type of attention deficit disorder in this regards.
Your comments
Of course federal procurement of R&D in decision-support technology is less than ideally rational, with entrenched interests, blockheads in high places, etc. I have had plenty of good proposals turned down. However, my current funding is adequate, and slowly growing, and I'm more interested in advancing my research, than in reforming the system.
echos the comments of thousands of others whose scientific interests are shaped by the funding system. And, the entire field is impoverished because relevant work on alternatives is not funded and only isolated individuals develop these alternatives. We are all faced with the “or” choice.
We must choose to work on things that are constant with AI philosophically or choose to make intellectual objections to what has happened to the foundations of computer science and its application to information technology.
The incorrectness of this work is not a personal attack on the dedicated efforts of thousands of PhDs in AI. But the incorrectness can be easily characterized. The computer processor does not “experience” the state transitions in silicon, it does not “know” the information that is constructed or moved. To act as if this is true is to begin the social dialog about intelligence based on unnecessary distortion. And when this distortion is used to glamorize DARPA or NIST R&D programs, the funding decisions lead to increased acceptance of this distortion.
Deductive inferences and deductive chains are abstractions that can and will diverge strongly from natural reality in complex systems like social and psychological systems. This was the core issue presented over and over at our three day conference at NIST. Many of the participants spoke to this core issue, while the two ladies from the AI division at the National Science Foundation where so kind as to correct a natural definition of what inference is, by scolding the notion that inference was anything other than as AI formally defines it to be.
Thus the work on understanding knowledge and perception from an inferential and computational perspective is likely to not find grounding in the natural sciences, in spite of the massive US federal funding that has occurred in support of this discipline.
This support is being called into question in a proposal to Establish the Knowledge Sciences.
Like the Manhattan Project, the National Educational Project to Establish the Knowledge Sciences is designed to change the nature of the social discourse. It is said that the Manhattan Project changed the nature of war. The National Educational Project could change the nature of information technology and mass media; by bringing a deep and natural understanding of computer science and mathematics in line with the natural sciences. { + }
As Roger Penrose, and others, have said a number of times, computational systems can be an aid to understanding some but not all of the realities of knowledge and the experience of knowledge by humans. We suggest that the engineering approach can be augmented to allow the natural sciences to inform us about what is real. But one might wish to look for a first principle that is not captured by formal constructions, something like inductive processes that are not reducible to deduction but which rely on direct measurement of reality as it is in a present moment.
Implicit in your note is the statement that a departure from the AI myth or from softer forms of the AI myth, such as cognitive engineering as defined by funding sources, will turn the funding off for you and your students. This phenomenon is to be the subject of a Congressional inquiry. (being considered as of 3/19/2004)
We strongly feel that the exclusive, and highly funded, AI paradigm reinforces the problem of abduction to false premise. If the computational inferences, which must be deductive, are composed into complex chains without human-centric inductive inferences, then we have a foundational problem leading to false sense making.
Abduction, defined as you have as a computational inference to best solution can be used to support unwarranted conclusions. As discussed by others at the Friends of Intelligence Community meeting, a premature closure can and is often occurring in real everyday intelligence analysis. If human judgment is characteristically left out or is subjected to dominance by computational outcomes (abductions, as you call this), then the premature closure can be re-enforced and the level of false sense making increased.
The literature on computation and information and computational inductive inference is extensive and has been highly supported since the early 1950s. The alterative paradigms have not been supported.
Although a literature of computational politics exists and continues to
grow, there is no evaluative literature to distinguish sound models from
spurious ones. This section adduces methodological concerns to guide model
building and to warn against potential pitfalls. {*}
The breakthrough has to be supported by legal and political means and enabled by unexpected technical solutions.
Consideration is made in using Waste Fraud and Abuse laws in order to demand that the funding system have more transparency and be required to take responsibility for large expenditures that end up being rejected by the community of intelligence analysts as being more of the same poorly designed and poorly performing software.
Honesty about how federal dollars are spent is required. The Nation expects this from the federal expenditures on science.
The dollars can be spent more wisely and achieve greater social benefit if the artificial maintenance of the artificial intelligence academic community is eliminated. We think that the first wave of new technical solutions has been found, by separating syntax and semantics. The result is called Human-centric Information Processing (HIP). Many of the AI faculty will embrace HIP, but some will not. But this evolution is one that, like the evolution from horse and buggy to automobiles, must occur due to social need.
We are sensitive to the harm that comes to individuals who make any public indication that the system perhaps needs to see reform. We take this environment to be an indication that a high degree of what was regarded, as freedom has been lost over the part decade. Legal action may be the only means to counter this phenomenon.
Our interest is in restoring a degree of true freedom and honesty to the R&D procurement process, so that the Nation and the World will enjoy future benefits.
The bead game’s home page has been shifted to:
Dr. Paul Prueitt
Director BCNGroup
703-981-2676
-----Original Message-----
From: J
Sent: Saturday, March 20, 2004 6:04 PM
To: Paul S Prueitt
Subject: Re: model of innovation expression
> J
>
> We are beginning to describe a model of innovation expression as a
> type of
> memetic expression with substructural elements being aggregated under
> certain sets of constraints.
>
Paul,
Thanks for the invitation. The topic is interesting, but does not draw
my passion right now. My current passions are in understanding
knowledge and perception from an inferential and computational
perspective, and in building related technology. Evolutionary models
and dynamical systems are rather tangential to my current work.
Of course federal procurement of R&D in decision-support technology is
less than ideally rational, with entrenched interests, blockheads in
high places, etc. I have had plenty of good proposals turned down.
However, my current funding is adequate, and slowly growing, and I'm
more interested in advancing my research, than in reforming the system.
.. j
http://www.bcngroup.org/beadgames/techInnovation/two.htm
>
>
> We would like very much if you would contribute to this discussion, as
> it
> develops.
>
>
>